Nature of the porosity in American coals

The nature of the porosity in a number of 40 × 70 (i.e. − 40 + 70) mesh size American coals, varying in rank from anthracite to lignite, has been studied using the following characterization techniques: gas adsorption, helium and mercury displacement, and mercury porosimetry. Surface areas calculated from carbon dioxide adsorption at 298 K are consistently higher than those calculated from nitrogen at 77 K, indicating the molecular sieve character of the coals. Total pore volumes have been measured in the diameter range 12–29 600 Å and then divided into macropores (300-29 600 Å), transitional pores (12–300 Å), and micropores (4–12 Å). Coals vary widely in their total pore volumes (porosities range between 4.1 and 23.2%). In the lower-rank coals (carbon content less than 75%), porosity is primarily due to the presence of macropores. In coals having a carbon content in the range 76–84%, about 80% of the total open pore volume is due to micro and transitional pores, whereas in the coals of higher carbon content microporosity predominates. Coals having about 35–55% of their total open pore volume in the transitional range are expected to be most suitable for use for adsorption of organic molecules from solution. Thus, they are of interest as possible materials to be used in water purification. In general, these results are considered to be of importance in understanding the extent and rate of interaction of coals with gases and liquids.     

Selection criteria of induction machines for speed-sensorless drive applications

Induction motors, both three and single phase, are used extensively for adjustable-speed drives' applications. These machines are structurally very robust and are a primary source of motive power and speed control where DC machines cannot be used. For closed-loop control of these machines, sensorless speed estimation is usually preferred. Among the current estimation techniques available for speed-sensorless induction motor drives, speed measurement based on rotor-slot-related harmonic detection in machine line current happens to be a prominent one. While these harmonics can be strong in certain kinds of machines, some other machines may exhibit very weak rotor slot harmonics that can be obscured by noise. Skewing, slot shapes and types, structural unbalances, etc., also have a prominent effect on the detectability of these harmonics. This paper attempts to investigate this problem based on the interaction of pole pairs, number of rotor bars, and stator winding. Although the analysis and experimental results have been mainly provided for three-phase squirrel-cage induction motors, single-phase and slip-ring induction motors have also been addressed. Further, it has been shown that eccentricity-related fault detection could also be easily accommodated with this kind of speed detection technique at no or negligible extra cost when certain motors are selected.     

Real-time classification of rotating shaft loading conditions usingartificial neural networks

Vibration analysis can give an indication of the condition of a rotating shaft highlighting potential faults such as unbalance and rubbing. Faults may however only occur intermittently and consequently to detect these requires continuous monitoring with real time analysis. This paper describes the use of artificial neural networks (ANNs) for classification of condition and compares these with other discriminant analysis methods. Moments calculated from time series are used as input features as they can be quickly computed from the measured data. Orthogonal vibrations are considered as a two-dimensional vector, the magnitude of which can be expressed as time series. Some simple signal processing operations are applied to the data to enhance the differences between signals and comparison is made with frequency domain analysis.     

Benefits of oxygen enrichment in sponge iron rotary kiln emerging from data-driven modelling

Optimal oxygen enrichment conditions for sponge iron rotary kiln have been successfully explored on an industrial scale using a data-driven model. A multi-objective optimisation by genetic algorithm (MOGA) is employed to find the favourable conditions. The objective function for MOGA is derived from neural networks using pre-processed operational data. From industrial experimentations guided by the optimum conditions predicted by the present model, it emerged that when the coal fines injection is maintained at 1.75?tph and the oxygen enrichment is 8 Nm³?t^?1 of sponge iron, a reduction in the specific air requirement from 2609 to 2150?Nm³?t^?1 was obtained, while the end-zone bed temperature remained under control at 1132°C. These conditions resulted in a reduction of specific coal consumption by 6%, an enhancement in the sponge iron production by 6% and an increase in the rotary kiln campaign life from 50 to 100 days.     

Water‐soluble ionic polythiophenes for biological and analytical applications

Ionic polythiophenes are important conjugated polymers because of their excellent optical properties and water solubility. They are classified as cationic, anionic and zwitterionic conjugated polyelectrolytes. This review article describes concisely their biological and analytical applications. The specific detection of different negatively charged biomolecules such as DNA and adenosine triphosphate, anions like halides and toxic pseudo‐halide (CN ^?) and environmental pollutants, e.g. surfactants, is discussed. The conformational changes of cationic polythiophenes (CPTs ) induced by various analytes due to formation of ionic conjugates and the cooperative responses of all segments cause dominant signal amplification even in the presence of a small perturbation. In addition, reactive oxygen scavenging, antimicrobial photosensitizing and cell imaging applications of CPTs are documented. Use of anionic polythiophenes for sensing of protamine and cations like Cu²⁺ and Ca²⁺ is also discussed. Finally, sensing of DNA , peptides and surfactants by zwitterionic polythiophenes is included. The concluding part discusses future prospects. © 2016 Society of Chemical Industry     

Selective greedy routing: exploring the path diversity in backbone mesh networks

This paper proposes a new routing protocol for IEEE 802.11s wireless mesh network to overcome the problems associated with the conventional proactive and reactive routing approaches. The proposed ‘Selective Greedy’ (SelG) routing protocol operates in two phases. In the first phase it exploits the proactive mode of Hybrid Wireless Mesh Protocol, the standard routing protocol for IEEE 802.11s mesh, and constructs a set of potential forwarders for every mesh point, that can act as the next-hop relay. In the second phase, during the actual data communication, a candidate is selected from the set of potential forwarders based on a local optimization. The optimization procedure considers the statistical effect of local link quality fluctuation and interference over the global routing path selection. This way the SelG protocol reduces the control packet flooding in the network (a major drawback for reactive protocols). At the same time, the optimization procedure captures the network dynamics, and thus avoids the possibility of routing based on stale information (a drawback for proactive protocols). The routing properties and the correctness of the SelG protocol is established theoretically, and the performance of the protocol is analyzed through simulation results. The proposed protocol is implemented in an indoor wireless mesh testbed, and the performance is evaluated and compared with other traditional approaches.     

Topology Management Ensuring Reliability in Delay Sensitive Sensor Networks with Arbitrary Node Failures

The lifetime of a sensor network is influenced by the efficient utilization of the resource constrained sensor nodes. The tree-based data gathering offers good quality of service (QoS) for the running applications. However, data gathering at the sink reduces the network lifetime due to a fast failure of highly loaded nodes. Loss of connectivity and sensing coverage affect the performance of the applications that demand critical QoS. In this paper, a data gathering tree management scheme has been proposed to deal with arbitrary node failures in delay-sensitive sensor networks. A load-balanced distributed BFS tree construction procedure has been introduced for an efficient data gathering. Based on the initial tree construction, a tree maintenance scheme and an application message handler have been designed to ensure the reliable delivery of the application messages. The correctness of the proposed scheme has been verified both theoretically and with the help of simulation. The proposed scheme offers low overhead, enhanced network lifetime and good QoS in terms of delay and reliability of the application messages.     

紫菜酶解产物锌螯合-α-葡萄糖苷酶抑制剂活性肽的研究

研究紫菜酶解α-葡萄糖苷酶抑制活性肽与锌螯合反应的条件,并对锌-螯合-糖苷酶抑制剂活性肽的胃肠消化稳定性进行评价。对条斑紫菜在一定条件下进行内切与外切蛋白酶的复合酶解获得具备α-葡萄糖苷酶高抑制活性的多肽,采用超滤纳滤双膜组合分离后旋转蒸发浓缩冻干,获得的多肽质量浓度为1 mg/mL时对0.5 mg/mL的α-葡萄糖苷酶抑制率达68%;利用α-葡萄糖苷酶抑制活性肽与锌溶液反应进行螯合条件优化,螯合的最佳条件为：时间1.5 h,pH 4.5,温度37 ℃,质量浓度6 mg/mL,得到的锌-螯合-糖苷酶抑制剂活性肽溶液螯合度为25.6%,螯合后对α-葡萄糖苷酶抑制活性提高到79.8%;建立体外胃肠消化模型,以α-葡萄糖苷酶抑制活性为指标,评价制备的锌-螯合-糖苷酶抑制剂活性肽的胃肠消化耐受性,结果表明：经过不同酶与底物比、时间胃消化后α-葡萄糖苷酶抑制活性下降均在7%左右,十二指肠消化后,抑制活性下降均在5%以内,具有良好的胃肠消化稳定活性。     

Facile Suzuki coupling over ortho-metalated palladium(II) complex anchored on 2D-hexagonal mesoporous organosilica

Phenyl functionalized 2D-hexagonal mesoporous silica material has been synthesized by cationic/non-ionic mixed surfactant templating route. The phenyl group of this mesoporous material is further functionalized via nitration and then reduction of that nitro group to amino functionality, followed by Schiff base condensation and heterogenization of a palladium(II) complex, yielded an ortho-metalated palladium(II) complex anchored in a ordered mesoporous silica matrix. This supported metal complex acts as an efficient catalyst in the Suzuki cross-coupling reaction and shows high selectivity for the bi-aryl products.